The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting
Abstract
Common spatial pattern (CSP) is a popular algorithm for classifying electroencephalogram (EEG) signals in the context of brain-computer interfaces (BCIs). This paper presents a regularization and aggregation technique for CSP in a small-sample setting (SSS). Conventional CSP is based on a sample-based covariance-matrix estimation. Hence, its performance in EEG classification deteriorates if the number of training samples is small. To address this concern, a regularized CSP (R-CSP) algorithm is proposed, where the covariance-matrix estimation is regularized by two parameters to lower the estimation variance while reducing the estimation bias. To tackle the problem of regularization parameter determination, R-CSP with aggregation (R-CSP-A) is further proposed, where a number of R-CSPs are aggregated to give an ensemble-based solution. The proposed algorithm is evaluated on data set IVa of BCI Competition III against four other competing algorithms. Experiments show that R-CSP-A significantly outperforms the other methods in average classification performance in three sets of experiments across various testing scenarios, with particular superiority in SSS.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Kittler, J., Hatef, M., Duin, R. P. W., & Matas, J. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.667881 · Cited by 5,290
Abe, S. (2001) · Springer eBooks · doi:10.1007/978-1-4471-0285-4 · Cited by 3,721
A well-conditioned estimator for large-dimensional covariance matrices
Ledoit, O., & Wolf, M. (2004) · Journal of Multivariate Analysis · doi:10.1016/s0047-259x(03)00096-4 · Cited by 3,046
Optimal spatial filtering of single trial EEG during imagined hand movement
Ramoser, H., Muller-Gerking, J., & Pfurtscheller, G. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.895946 · Cited by 2,457
Regularized Discriminant Analysis
Friedman, J. H. (1989) · Journal of the American Statistical Association · doi:10.1080/01621459.1989.10478752 · Cited by 2,421
Optimizing Spatial filters for Robust EEG Single-Trial Analysis
Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., & Muller, K.-R. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.4408441 · Cited by 2,080 · cited 16×
“inexpensiveness, and high temporal resolution, it is widely used in noninvasive brain–computer interfaces (BCI) [2], [3], where”
BackgroundMethodInfluentialThe Boosting Approach to Machine Learning: An Overview
Schapire, R. E. (2003) · Lecture Notes in Statistics · doi:10.1007/978-0-387-21579-2_9 · Cited by 2,003
Sanei, S., & Chambers, J. A. (2007) · Wiley eBooks · doi:10.1002/9780470511923 · Cited by 1,552
Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface
Ang, K. K., Chin, Z. Y., Zhang, H., & Guan, C. (2008) · 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) · doi:10.1109/ijcnn.2008.4634130 · Cited by 1,514
Self-citationMathematical Methods and Algorithms for Signal Processing
Moon, T. K., & Stirling, W. C. (1999) · Cited by 1,491
Cited by
Lotte, F. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2404941 · Cited by 327
Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review
Saha, S., & Baumert, M. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00087 · Cited by 325
Internal Feature Selection Method of CSP Based on L1-Norm and Dempster–Shafer Theory
Jin, J., Xiao, R., Daly, I., Miao, Y., Wang, X., & Cichocki, A. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3015505 · Cited by 267
Metadata
- Authors
- Lu, H., Eng, H.-L., Guan, C., Plataniotis, K. N., & Venetsanopoulos, A. N.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2010-12
- Volume
- 57
- Issue
- 12
- Pages
- 2936-2946
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2155617375
- Cited by
- 309
- References
- 36
- FWCI
- 4.89
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting. https://app.cochat.ai/papers/10.1109/tbme.2010.2082540
- The paper
- Lu, H., Eng, H.-L., Guan, C., Plataniotis, K. N., & Venetsanopoulos, A. N. (2010). Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2010.2082540
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.